Senior Data Scientist

Role: Senior Data Scientist / ML Engineer with AWS

Location: Remote

Summary: Works on data to build, train and deploy next-generation LLM-based recommender systems. The role covers the full path from raw customer data to a deployed model.
Responsibilities:

· Data to training sets: turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.

· Feature and context engineering: decide what information goes into each document and how to express it, such as market-relative price, tier and trip context.

· Large-scale distributed training on Amazon SageMaker AI, including HyperPod.

· Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.

· Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.

· Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling.

· Evaluation and error analysis: build evaluation sets, run ablations and catch data leakage. Write up findings clearly for the customer and the AWS science team.

· Data privacy: handle customer data safely, including removing per

Required skills

· Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents.

· Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment.

· LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods).

· Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training.

· Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch).

· Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation.

· Clear written communication with customer and science stakeholders.

Preferred skills

· AWS Trainium / Inferentia (Neuron SDK).

· Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders.

· Embedding quantization or semantic IDs.

· Experience in a customer-facing or embedded role.